Search

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the 'phenofit' R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan's analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event's impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery. [ABSTRACT FROM AUTHOR]

Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra were obtained by spectral denoising followed by spectral transformation. Next, sensitive bands (Rλ), spectral indices (SIs), and PPs were extracted from all four spectra at each growth stage. Then, univariate models were constructed to determine their potential for independent LCC estimation. The multivariate regression models for the LCC (LCC-MR) were built based on SIs, SIs + Rλ, and SIs + Rλ + PPs after feature variable selection. The results indicate that our machine-learning-based LCC-MR models demonstrated high overall accuracy. Notably, 83.33% and 58.33% of these models showed improved accuracy when the Rλ and PPs were successively introduced to the SIs. Additionally, the model accuracies of the milk-ripe and tasseling stages outperformed those of the flare–opening and jointing stages under identical conditions. The optimal model was created using XGBoost, incorporating the SI, Rλ, and PP variables at the R3 stage. These findings will provide guidance and support for maize growth monitoring and management. [ABSTRACT FROM AUTHOR]

Rice is one of the main food crops in China. It is of great importance for the timely, rapid, and accurate acquisition of rice field area and its spatial distribution in agricultural production, particularly for national food security. However, the traditional classification variables of remote sensing and pixel-based machine learning have posed great challenges in identifying the paddy fields with high fragmentation. The phenological parameters have been used to represent the growth dynamics of vegetation. Object-oriented random forests can also effectively avoid the 'salt and pepper' noise for the high accuracy of paddy fields. In this study, the object-oriented algorithm was selected to identify the paddy field, according to the rice phenological parameters. The research area was taken as the paddy field in Zhangzhou City, Fujian Province, a typical mountainous and hilly area in southern China. Firstly, the recursive feature elimination (RFE) and variance inflation factor (VIF) were applied to optimize the remote sensing variables. As a result, the normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), soil-adjusted vegetation index (SAVI), vertical transmit/vertical receive (VV), vertical transmit/horizontal receive (VH), and six phenological parameters were taken to construct the classification model. Secondly, the object-oriented and pixel-based random forest was used to identify the paddy fields. Three combinations of the variables were used as the input parameters of the classification model. The effectiveness of phenological parameters and the object-oriented random forest were verified to improve the accuracy of paddy field identification in the complex terrain areas in southern China. The results are as follows: 1) The highest identification accuracy of paddy fields was 94.47%, where the Kappa coefficient was 0.92. A better performance was achieved to identify the paddy fields using the remote sensing variables, phenological parameters, and object-oriented random forest; 2) The phenological parameters shared the significant advantages to characterizing the vegetation growth among various types. Thus, the phenological parameters and remote sensing variables were combined to improve the accuracy of paddy field identification by 8.78-9.36 percentage points, compared with the experimental group with remote sensing variables only; 3) The object-oriented random forest was outstanding defined the shape and boundary of paddy fields with a high degree of fragmentation in the complex terrain areas. The artifact of 'salt and pepper' was effectively avoided. The object-oriented classification improved the accuracy of paddy fields by 0.58-1.53 percentage points, compared with the traditional pixel-based one. Therefore, the improved model was more suitable for the extraction of fragmented farmland in the complex topographic areas using remote sensing images. The finding can provide a strong reference to further improve the accuracy of paddy field mapping products in complex terrain areas. [ABSTRACT FROM AUTHOR]

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery.

Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra were obtained by spectral denoising followed by spectral transformation. Next, sensitive bands (Rλ), spectral indices (SIs), and PPs were extracted from all four spectra at each growth stage. Then, univariate models were constructed to determine their potential for independent LCC estimation. The multivariate regression models for the LCC (LCC-MR) were built based on SIs, SIs + Rλ, and SIs + Rλ + PPs after feature variable selection. The results indicate that our machine-learning-based LCC-MR models demonstrated high overall accuracy. Notably, 83.33% and 58.33% of these models showed improved accuracy when the Rλ and PPs were successively introduced to the SIs. Additionally, the model accuracies of the milk-ripe and tasseling stages outperformed those of the flare–opening and jointing stages under identical conditions. The optimal model was created using XGBoost, incorporating the SI, Rλ, and PP variables at the R3 stage. These findings will provide guidance and support for maize growth monitoring and management.

Vegetation phenology reflects the response mechanisms in ecology and climate change, so it is important that the parameters used to study vegetation phenology are accurate. Previous studies mainly focused on phenological changes. However, because the extraction methods used in those investigations led to inconsistencies in setting vegetation phenological parameters, a more accurate approach needs to be developed. To resolve this issue, we select five methods to extract the start of the growing season (SOS) and the end of the growing season (EOS) from the normalised difference vegetation index (NDVI3g) data. The five chosen methods are the second-order derivative method (Method 1), the first-order derivative method (Method 2), the 0.2 dynamic threshold method (Method 3), the 0.5 dynamic threshold method (Method 4), and the fixed threshold method (Method 5). Our study area is the Northern Hemisphere (above 30°N), and our study period is 1982 to 2015. After applying the five methods, we evaluate the consistency of the vegetation phenological parameters. The results show that (1) regardless of the method used, the average changes in phenological parameters are consistent; however, the SOS and EOS under Methods 1, 3 and 5 are up to 30 days earlier than those under Methods 2 and 4. (2) Under all five methods, the SOS trend mainly shows an advance, but the trend is substantially higher under Methods 1, 3 and 4 than under Methods 2 and 5 from 45°N to 60°N. The distribution of the EOS trend under different methods is consistent. (3) Under the tested extraction methods, the SOS trends of evergreen needleleaf forests (ENF) and mixed forests (MF) have significant differences (p < 0.05), whereas, the EOS trend for different vegetation types is consistent. (4) By analysing the consistency of the phenological parameters between remote sensing data and ground data under different methods, we now know that Methods 3 and 4 are the most accurate for extracting the SOS and EOS, respectively. The above results can provide a reference for the accurate extraction of phenological parameters above 30°N. [ABSTRACT FROM AUTHOR]

The effectiveness of a Marine Protected Area (MPA) is strongly related to its zoning design, management, and surveillance, and fish communities are considered indicators of conservation effects. Posidonia oceanica beds and fish assemblages were studied here to evaluate the fishing impacts and the effectiveness of the Marine Protected Areas (MPAs) of Santa Maria di Castellabate (SMC) and Costa degli Infreschi e della Masseta (CIM) in the South Tyrrhenian Sea, Italy. P. oceanica characteristics were estimated counting and collecting shoots, while fish communities were investigated by visual census. Multivariate analyses were performed to detect differences in fish assemblages among the MPA protection levels and seagrass characteristics. Significant differences in fish communities were observed between the depth and protection levels. The latter are probably due to the zoning design at SMC, which does not allow a spillover effect, and to fishing activities, as showed by the ABC curve results. Trophic analyses suggested the use of longlines at SMC, due to the dominance of planktivorous fish species, and of trammel nets at CIM, due to the great heterogeneity of the fish trophic groups. Shoot density and leaf length were significantly correlated with fish communities. CCA analyses showed omnivorous species, mostly belonging to the Sparidae family, correlated with the highest shoot densities, with their feeding habits mostly directed to crustaceans, while macro- and microinvertivores, mainly belonging to the Serranidae and Labridae families, correlated with the highest leaf lengths, where they sough shelter and feed. In conclusion, the data suggest that a zoning redesign at SMC and an increase in the surveillance of fishing activities at CIM might enhance the effectiveness of these MPAs, highlighting the role of fish community studies in identifying MPA efficiency issues and providing trustful guidelines for their management. [ABSTRACT FROM AUTHOR]

While crop models are increasingly applied to large-scale areas, inadequate observation data make it difficult to calibrate the model's phenological parameters at the regional scale. The present study proposed a simple mathematical method for estimating rice phenological parameters across spatial scales for the ORYZA model. The method establishes the cumulative function of the phenological parameters (CFPP). By fitting the CFPP with the so-called growth curve, the 4 phenological parameters of the ORYZA model were transformed into 3 fitted parameters in the equation of CFPP. Functions between the fitted parameters and several meteorological and field management factors were established. These established functions were substituted back into CFPP to construct a modified CFPP. Due to the inter-translational relationship between CFPP and the original phenological parameters, the values of phenological parameters could be estimated by the modified CFPP based on meteorological and field management factors. The newly proposed mathematical method was applied in the Yangtze River Basin (YRB), China. The results indicated that the multi-station average of the absolute value of relative errors for the rice panicle initiation, flowering, and maturity dates within the YRB were 12.3 %, 10.5 %, and 8.7 %, respectively, which were at most 4.8 % larger than that simulated using parameters calibrated using each station's phenological data. The phenological parameters estimated by the novel mathematical method had close performance to those calibrated directly based on observed data at most stations in terms of rice phenology simulation. The present study provided a new solution for phenological parameter calibration for crop models when applied in a large-scale area. [Display omitted] • Cumulative function of phenological parameters (CFPP) for the ORYZA model was constructed. • The phenological parameters and CFPP can be transformed into each other. • The spatial variation of parameters in CFPP were investigated. • A modified CFPP for estimating the phenological parameters across regional scales was constructed. [ABSTRACT FROM AUTHOR]

• Interactive effects of different temperatures (15 °C and 40 °C) different boric acid concentrations (10 and 25 mM) were investigated in this study. • Phenological parameters, MDA level, SOD, CAT, APX activities and gene expressions were determined. • Boric acid application increased the negative effect of high temperature stress on all parameters examined. • It has been determined that boron supplementation in the plant is an important factor in tolerating low temperature stress. Plants have to cope with more than one abiotic stress at the same time in nature. Heat stress and mineral toxicity are at the forefront of these abiotic stresses. The purpose of this study is to investigate the interactive effects of high and low-temperature stress and boric acid application on the sunflower and reveal the potential protection mechanisms against the combined effect of two types of stress. Two different boric acid concentrations (10 and 25 mM) were applied to sunflowers grown separately at low (15 °C), optimum (25 °C) and high (40 °C) temperature. Then, root length and stem height, root and stem fresh-dry weight, root and stem biomass, malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT) and ascorbate peroxidase (APX) enzyme activities and changes in the gene expression levels of these enzymes were determined. It was determined that there were reductions in the phenological properties of the roots and stems of the samples grown at 40 °C based on the increasing boric acid concentration, while the samples grown at 15 °C were positively affected. The antioxidant enzyme activities and mRNA levels of the root and leaf samples at 40 °C were observed to increase. For the samples at 15 °C, the antioxidant enzyme activities and mRNA levels increased in the roots and decreased in the leaves. Considering phenological parameters, it was revealed that high boric acid application at low temperatures promoted plant growth, and considering the antioxidant enzyme activities and mRNA expression levels, it was highly effective in the plant's toleration of low temperatures. [ABSTRACT FROM AUTHOR]

Vegetation phenology reflects the response mechanisms in ecology and climate change, so it is important that the parameters used to study vegetation phenology are accurate. Previous studies mainly focused on phenological changes. However, because the extraction methods used in those investigations led to inconsistencies in setting vegetation phenological parameters, a more accurate approach needs to be developed. To resolve this issue, we select five methods to extract the start of the growing season (SOS) and the end of the growing season (EOS) from the normalised difference vegetation index (NDVI3g) data. The five chosen methods are the second-order derivative method (Method 1), the first-order derivative method (Method 2), the 0.2 dynamic threshold method (Method 3), the 0.5 dynamic threshold method (Method 4), and the fixed threshold method (Method 5). Our study area is the Northern Hemisphere (above 30°N), and our study period is 1982 to 2015. After applying the five methods, we evaluate the consistency of the vegetation phenological parameters. The results show that (1) regardless of the method used, the average changes in phenological parameters are consistent; however, the SOS and EOS under Methods 1, 3 and 5 are up to 30 days earlier than those under Methods 2 and 4. (2) Under all five methods, the SOS trend mainly shows an advance, but the trend is substantially higher under Methods 1, 3 and 4 than under Methods 2 and 5 from 45°N to 60°N. The distribution of the EOS trend under different methods is consistent. (3) Under the tested extraction methods, the SOS trends of evergreen needleleaf forests (ENF) and mixed forests (MF) have significant differences (p < 0.05), whereas, the EOS trend for different vegetation types is consistent. (4) By analysing the consistency of the phenological parameters between remote sensing data and ground data under different methods, we now know that Methods 3 and 4 are the most accurate for extracting the SOS and EOS, respectively. The above results can provide a reference for the accurate extraction of phenological parameters above 30°N.

The effectiveness of a Marine Protected Area (MPA) is strongly related to its zoning design, management, and surveillance, and fish communities are considered indicators of conservation effects. Posidonia oceanica beds and fish assemblages were studied here to evaluate the fishing impacts and the effectiveness of the Marine Protected Areas (MPAs) of Santa Maria di Castellabate (SMC) and Costa degli Infreschi e della Masseta (CIM) in the South Tyrrhenian Sea, Italy. P. oceanica characteristics were estimated counting and collecting shoots, while fish communities were investigated by visual census. Multivariate analyses were performed to detect differences in fish assemblages among the MPA protection levels and seagrass characteristics. Significant differences in fish communities were observed between the depth and protection levels. The latter are probably due to the zoning design at SMC, which does not allow a spillover effect, and to fishing activities, as showed by the ABC curve results. Trophic analyses suggested the use of longlines at SMC, due to the dominance of planktivorous fish species, and of trammel nets at CIM, due to the great heterogeneity of the fish trophic groups. Shoot density and leaf length were significantly correlated with fish communities. CCA analyses showed omnivorous species, mostly belonging to the Sparidae family, correlated with the highest shoot densities, with their feeding habits mostly directed to crustaceans, while macro- and microinvertivores, mainly belonging to the Serranidae and Labridae families, correlated with the highest leaf lengths, where they sough shelter and feed. In conclusion, the data suggest that a zoning redesign at SMC and an increase in the surveillance of fishing activities at CIM might enhance the effectiveness of these MPAs, highlighting the role of fish community studies in identifying MPA efficiency issues and providing trustful guidelines for their management.

Changes in crop yields may have important implications for food security in Morocco. This study intends to develop an explicit model based solely on phenological parameters derived from Moderate-Resolution Imaging Spectroradiometer (MODIS)/NDVI data to monitor wheat grain yield. The developed model allows overcoming missing weather, soil and irrigation supply data without losing the spatial resolution offered by images data. The study period covers a 16-year span between 2000 and 2016, and the considered region is the north-western of Morocco. The model showed a good correlation with ground measurements (R2 = 0.62; p < 0.01). Spatio-temporal variability and trend of wheat yield were examined. The spatial analyses revealed an increase of instability of wheat grain yields across central and southern regions. Such a tool allows managers and policy makers to analyse the agricultural policy impact, to monitor the agronomic potential dynamic, to control the cropping season evolution and to optimize the land use choices. [ABSTRACT FROM AUTHOR]

There is a huge demand for ELS cotton (Gossypium barbadense L.) in India for its premium fibre properities, however, the area and production of ELS is very less.There is a considerable scope for extending the area of ELS under rainfed condition to increase the production. But the fibre quality got severely affected due to moisture stress in rainfed condition as the crop has long phase of growing period. Hence the experiment is conducted to ascertain the relationship between phenological periods with quality parameters to identify sensitive phenological periods for better crop management and also to assess the production potential of ELS genotypes compared with hirsutum hybrids. Amongst genotypes, seed cotton yield (kg/ha) was significantly higher with RCH659 BG II (H × H) (2839 kg/ha) The quality parameters results revealed that suvin non Bt showed the significantly highest fibre length (39.3 mm of 2.5% span length) and fibre strength (32.0 g/tex). Fibre quality was superior in early formed bottom bolls compared to middle and top bolls. Quality traits were highly correlated with duration of the crop as duration gets shorter, quality becomes deteriorated. The duration of the first squaring, 50% flowering, and 50% boll bursting were positively and significantly correlated with fibre length and strength . The duration of 50% squaring to first flowering was significantly positively correlated with uniformity ratio.The economic analysis showed that inter specific hybrid MRC 7918 BG II registered the highest net return (₹ 116916/ha) which is better than intra-hirsutum hybrid RCH659 BG II (H×H)( ₹ 99332/ha).

Soil salinity is one of the main factors limiting cereal productivity in worldwide agriculture. Exploitation of natural variation in local barley germplasm is an effective approach to overcome yield losses. Three gene pools of North African Hordeum vulgare L. grown in Tunisia, Algeria and Egypt were evaluated at the reproductive stage under control and saline conditions. Assessment of stress tolerance was monitored using morphological, yield-related traits and phenological parameters of reproductive organs showing significant genetic variation. High heritability and positive relationships were found suggesting that some traits associated with salt tolerance could be used as selection criteria. The phenotypic correlations revealed that vegetative traits including shoot biomass, tiller number and leaf number along with yield-related traits such as spike number, one spike dry weight, grain number/plant and grain number/spike were highly positively correlated with grain yield under saline conditions. Hence, these traits can be used as reliable selection criteria to improve barley grain yield. Keeping a higher shoot biomass and longer heading and maturity periods as well as privileged filling ability might contribute to higher grain production in barley and thus could be potential target traits in barley crop breeding toward improvement of salinity tolerance. Multiple selection indices revealed that salt tolerance trait index provided a better discrimination of barley landraces allowing selection of highly salt-tolerant and highly productive genotypes under severe salinity level. Effective evaluation of salt tolerance requires an integration of selection indices to successfully identify and characterize salt tolerant lines required for valuable exploitation in the management of salt-affected areas.

Plant’s health status is one of the most important indicators to evaluate restoration projects and environmental evolution of degraded land, particularly in mine restoration areas. Most studies on vegetation cover changes did not consider the phenological characteristics and health status in remote sensing monitoring, although optical remote sensing technology has been available since the 1980s. Taking a basalt stone pit in Qitaihe City, Heilongjiang Province, China as the research area, this study aims to detect the health status of corn crops using remote sensing. Thirteen images were captured from the Sentinel-2 satellite from April to October 2020, and then reconstructed by the time-series of Normalized Difference Vegetation Index (NDVI). Different fittings were selected, including the Savitzky-Golay (S-G) filtering, double logistic (D-L) fitting, and asymmetric Gaussian (A-G) filtering. Dynamic thresholds and curvatures were used, where the phenological indicators of corn crops covered the periods of emergence, jointing, tasseling, and maturity, as well as the interval length between phenological periods. An analysis was made on the difference of corn phenological characteristics between the mine restored and normal cultivated areas. Some remote sensing spectral indices were extracted, including the Red-Edge Inflection Point (REIP), NDVI, green NDVI (GNDVI), Meris Terrestrial Chlorophyll Index (MTCI), pigment specific simple ratio (PSSRA), Inverted Red-Edge Chlorophyll Index (IRECI), Modified Chlorophyll Absorption Ratio Index (MCARI) and vegetation coverage (FVC). A systematic evaluation was made on the feasibility and sensitivity of indices to distinguish between healthy crops and sub-health crops in the restoration area. The results showed that the key phenological periods of corn was delayed in the restoration treatment area, where the emergence period was delayed by 5-12 days, while the jointing period was delayed by 9-12 days, and the maturity period was delayed by 21-22 days, compared with those in the normal cultivated areas. The phenological difference of crops gradually emerged as the crop grew. Remote sensing indicators, including the REIP, NDVI, GNDVI, PSSRA, IRECI, and FVC, can effectively distinguish the crops spectral features and the health status difference between the mine restoration and normal cultivation areas. The GNDVI received the lowest standard deviation for the restoration area (0.093 5 for area A, and 0.056 2 for area B), indicating the most stable indicator of the overall health status of crops. In PSSRA amplitude interval of pixels, the lower limit in the C area was 12.61% and 44.72% higher than the upper limit in the A and B areas, respectively, indicating the most sensitive among the indicators. The spectral features were related to the comprehensive influence of physiological difference between restoration and normal cultivation areas, including the leaf green content, leaf area index, and leaf water content of corn crops. This finding can provide an insightful understanding to rapidly evaluate the mine restoration using remote sensing technologies. [ABSTRACT FROM AUTHOR]

This research was carried out in the in the Kahramanmaras Sutcu Imam University, Faculty of Agriculture (37°35'24.8"N 36°48'49.4"E) where the Mediterranean geographical region located in the south of Turkey in 2015-2016. In growing seasons five chickpea (Cicer arietinum L.) cultivars (Aksu, Azkan, Cagatay, Inci and Yasa-05) were tested in vitro and vivo conditions. In the research plant height, seed yield of a single plant, harvest index, nitrogen content of seed, fat ratio of seed, protein ratio of seed and calorific value of seed were investigated. Significant differences were found in all treatment except calorific value. [ABSTRACT FROM AUTHOR]

Soil salinity is one of the main factors limiting cereal productivity in worldwide agriculture. Exploitation of natural variation in local barley germplasm is an effective approach to overcome yield losses. Three gene pools of North African Hordeum vulgare L. grown in Tunisia, Algeria and Egypt were evaluated at the reproductive stage under control and saline conditions. Assessment of stress tolerance was monitored using morphological, yield-related traits and phenological parameters of reproductive organs showing significant genetic variation. High heritability and positive relationships were found suggesting that some traits associated with salt tolerance could be used as selection criteria. The phenotypic correlations revealed that vegetative traits including shoot biomass, tiller number and leaf number along with yield-related traits such as spike number, one spike dry weight, grain number/plant and grain number/spike were highly positively correlated with grain yield under saline conditions. Hence, these traits can be used as reliable selection criteria to improve barley grain yield. Keeping a higher shoot biomass and longer heading and maturity periods as well as privileged filling ability might contribute to higher grain production in barley and thus could be potential target traits in barley crop breeding toward improvement of salinity tolerance. Multiple selection indices revealed that salt tolerance trait index provided a better discrimination of barley landraces allowing selection of highly salt-tolerant and highly productive genotypes under severe salinity level. Effective evaluation of salt tolerance requires an integration of selection indices to successfully identify and characterize salt tolerant lines required for valuable exploitation in the management of salt-affected areas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982-2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982–2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas.

Copyright of Journal of Zhejiang University: Science B is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Near infrared band (sentinel-2) and SATVI are of great importance in predicting SOC. • Adding phenological parameters improved SOC prediction accuracy. • Crop phenological parameters were generated based on sentinel-2 images. • Contrast the effects of moisture index, bright-related index and vegetation index in SOC mapping. It is important to predict the spatial distribution of SOC accurately for migrating carbon emission and sustainable soil management. Environmental variables influence the accuracy of SOC prediction with digital soil mapping (DSM) approaches. In addition to the commonly-used natural predictors, remote sensing variables have been recently used in DSM. However, it is still challenging which variables are effective to predict SOC in farmland. Although phenological parameters have been recently used to indicate human activities that affect SOC in farmland, there are few studies that employ the phenological parameters in SOC prediction. Therefore, this study investigates the feasibility of SOC prediction with the phenological parameters and numerous remote sensing variables extracted from Sentinel-2 at high temporal and spatial resolutions. From 34 Sentinel-2 time series images from 2018 to 2019, 17 phenological parameters were extracted for Xuanzhou, Anhui Province using a dynamic threshold method. Furthermore, fifteen remote sensing predictors comprised of vegetation indices, bright-related indices, and moisture indices were generated from the Sentinel-2 images. The phenological parameters and remote sensing variables were combined with natural variables to predict SOC contents at the surface soil layer using random forest. The results showed that the auxiliary parameters, i.e., the phenological parameters and remote sensing predictors, enhanced the predictability of SOC with an increase in R2 by 171% and a decrease in RMSE by 14%. This study also identified relatively more important auxiliary parameters for the SOC prediction: the largest data value for the fitted function during the season (a6), rate of increase at the beginning of the season (a8), large seasonal integral (a10), SATVI, and Band8. Therefore, this study verified that the phenological parameters and remote sensing predictors extracted from the Sentinel-2 EVI time series are effective for DSM in farmland. [ABSTRACT FROM AUTHOR]

• Phenological indices were derived from NDVI time series using a dynamic threshold method. • The base level was the most important variable for predicting SOC in this study area • Phenological indices with crop rotation type improved prediction of SOC in croplands. • The study demonstrates the effectiveness of including phenological indices in SOC prediction. Mapping the spatial distribution of soil organic carbon (SOC) content or stock is important for climate change studies and land management decisions. When using environmental covariates to map SOC content or stock, variables indicating human activities have drawn growing attentions. Crop species/crop rotations and agricultural management significantly affect the spatial variation of SOC in croplands. For areas where climatic conditions and farming managements are generally consistent in cultivation territory of one crop species, crop phenology largely indicates the crop response to soil. Therefore, phenological parameters incorporating with crop rotation could be effective for mapping soil organic carbon in these areas. In this study, we extracted phenological parameters from Normalized Difference Vegetation Index (NDVI) time series data, and used these variables with crop rotation for predicting topsoil organic carbon content in a cropland area in Anhui province, China. Forty-nine sampling points were collected in field. For these points, there were three crop rotations each with two crop species per year. Twenty-two HJ-1 A/B images for 2010 year with a 30 m resolution were obtained. Eleven phenological parameters for each of the two growing seasons were obtained with a dynamic threshold method. Various combinations of predictive variables were developed based on variable importance and experimented for predicting topsoil organic carbon using random forest. The prediction results were validated using a cross validation approach. Results showed that base levels (given as the average of the left and right minimum values of a time series profile) for both seasons were the most important predictors in this area. Adding both crop rotation and the two phenological parameters to the natural environment variables increased the prediction accuracies by 50% in terms of R2 and 13.4% in terms of root mean square error (RMSE). This study demonstrates the effectiveness of crop phenology in mapping SOC in croplands. [ABSTRACT FROM AUTHOR]